Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning Online publication date: Mon, 06-Apr-2020
by Mehdi Neshat; Ali Akbar Pourahmad; Zahra Rohani
International Journal of Swarm Intelligence (IJSI), Vol. 5, No. 1, 2020
Abstract: Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The simplified memory bounded A* (SMA*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.
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